Assessment of Cloud Fraction using Terra MODIS sensor data, 2007, Iran

Document Type : Full length article


1 PhD candidate in Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran

2 Associate Professor of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran


Clouds and water vapor are important modulators of climate and are involved in feedbacks that strongly affect global circulation and energy balance. Typically, 50% of the earth surface is covered by clouds, at any given time. A cloud is defined as a visible mass of condensed water droplets or ice crystals suspended in the atmosphere above the earth surface. The cloud phenomenon plays an important role in the water cycle, radiation, temperature, precipitation, predictors of climate, and etc. As there are not adequate information on the spatial distribution of clouds in Iran, except for synoptic stations data, it is necessary to conduct researches on the physical properties of cloud by remote sensing data. Therefore, these kinds of data can provide us with a more accurate and comprehensive understanding of cloud phenomenon. This study examines the spatial distribution of cloud Fraction (cover) over Iran in 2007 using remotely sensed data. The results of this fundamental research could be applied in practical knowledge such as weather forecasting, climate modeling, seeding clouds, site selection of solar panels, and etc.
Materials and methods
The aim of this research is to survey the spatial distribution of Cloud Fraction (CF) over Iran in annual and seasonal timescales. To do this, daily data of cloud product of MODIS Terra (MOD06_L2.A) over Iran was used in 2007. The data was obtained from the ftp link\  In this research, we have used spatial resolution of CF in 5 km×5 km scripting in MATLAB. In the first step, overlapping images have been removed and, then, the data within the Iranian border have been extracted from daily data. The data have been transferred into regular network of Iran for doing statistical computations.  
Results and discussion
The investigation on the annual mean percent of CF indicates that the value is 25.3% for the morning and 29.7%for afternoon. Thus, the amount of CF in the afternoon is increased compared to the morning times. In winter, the amount of CF for the morning is 48.2% but for the afternoon it is reduced to 44.1% and this reduction condition is not seen in other seasons. In spring, fall and winter seasons the amount of CF is increased in the afternoon compared to the morning. The spatial distribution of annual percent of CF indicates that the maximum is seen over the southern shores of the Caspian Sea and the minimum is observed in south east part of the country. The spatial distribution of seasonal percent of CF shows that the maximum amount of CF is over the highlands of Zagrous and Alborz mountains and the minimum is in south and south-east regions of the country. In this season, the maximum percent of CF is not seen over Caspian shores like other seasons. In spring, the maximum percent of CF can be seen over the southern shores of the Caspian Sea and parts of north-west but the minimum percent of CF can be observed over central areas of south and south-east regions of the country. In fall season, the maximum percent of CF is seen over the southern shores of the Caspian Sea and parts of north-east and the minimum is observed in south and south-east regions. In summer, the maximum percent of CF is in the southern shores of the Caspian Sea and the minimum can be seen over east and west parts of the country. In summer, the extent of minimum percent of CF is changed to other seasons and is far away from south-east regions.  
In this investigation, the CF parameter of MODIS Terra was applied in the daily temporal resolutions for the year 2007 to explore the spatial distribution of cloud cover over Iran. As the data did not have a regular geographical coordinated grid, a regular coordinate was initially constructed and CF data were trasnferred to this regular grid. This process was conducted to analyze the climatology of cloud cover. The results from MODIS Terra data for the pass of morning and afternoon times revealed that the maximum annual percent of CF is seen over the southern shores of the Caspian Sea and the minimum is occurred over south-east part of the country which is consistent with the results of Rasooli et al. (2013) and Masoodian and Kaviani (2008). For the seasonal time scales, the maximum percent of CF is occurred over the southern shores of the Caspian Sea for spring, fall and summer seasons but in winter it is seen over the elevations of Alborz and north-west parts of the country. The minimum percent of CF is seen in south-east and east parts of the country for spring, fall and winter seasons. In summer, it is observed over east and west regions where shows that the formation process of cloud is different in winter and summer compared to the other seasons. The validation of CF values in the annual time scale indicates that MODIS overestimes CF by 3% compared to the synoptic stations. This is acceptable when the results are compared with the findings of Bisoolli and Pahl (2001) in Germany with the erros of 6% and Kotarba (2009) in July and January months with the error of 4.38% and 7.28% for the year 2004. In general, the estimation of cloud cover is identical for the two data sets. 


Main Subjects

امیدوار، ک. (1380). تحلیل سینوپتیکی سیستم‏های باران‏زا و امکان افزایش بارش آن‏ها به وسیلة باروری ابرها در منطقة کرمان، پژوهش‏های جغرافیایی، 40: 19ـ32.
حاجی ‏میررحیمی، س. و بای، ن. (1387). طبقه‏بندی انواع ابر با استفاده از تصاویر AVHRR، همایش ژئوماتیک، تهران.
رسولی، ع.؛ جهان‏بخش، س. و قاسمی، الف. (1392). بررسی تغییرات زمانی و مکانی مقدار پوشش ابر در ایران، فصل‏نامة تحقیقات جغرافیایی، 3(28):85ـ102.
مرآت، الف (1386). مفاهیم و روش‏های آماری (1)، تهران، شرکت چاپ و نشر کتاب‏های درسی ایران.
مسعودیان، س.‏ا. و کاویانی، م. (1387). اقلیم‏شناسی ایران، اصفهان: انتشارات دانشگاه اصفهان.
Ackerman, S.A. and Platnick, S. (2011). Moderate Resolution Imaging Spectro‏-radiometer (MODIS) Cloud Fraction Technical Document (ATBD), pp.1-12.
Ackerman, S.A.; Strabala, K.I.; Menzel, W.P.; Frey, R.A.; Moeller, C.C. and Gumley, L.E. (1998). Discriminating clear sky from clouds with MODIS, Journal of Geophysical Research, 103: 32141-32157.
Asmala, A. and Shaun, Q. (2012). Cloud masking for remotely sensed data using spectral and principal components analysis, ETASR-Engineering, Technology and Applied Science Research, 2(3): 221-225.
Bissolli, P. and Pahl, U. (2001). Validation of the ISCCP satellite cloud cover climatology over Germany with synoptical data, ProcEUMETSAT Meteorological Satellite Data Users Conference, Antalya, 1-5 October.
Bissolli, P. and Pahl, U. (2001). Validation of the ISCCP satellite cloud cover climatology over Germany with synoptical data, Proc. 2001 EUMETSAT Meteorological Satellite Data Users Conference, Antalya, 1-5 October.
Chen, Ting; William B., Rossow,and Yuanchong, Zhang (2000). Radiative effects of cloud-type variations, Journal of climate13: 264-286.
Choi, Y.S.; Ho, C.H.; Kim, S.W. and Lindzen, R.S. (2010). Observational diagnosis of cloud phase in the winter Antarctic atmosphere for parameterizations in climate models, Advances in Atmospheric Sciences, 27(6): 1233-1245.
Filipiak, J. and Mietus, M. (2009). Spatial and temporal variability of cloudiness in Poland, 1971–2000, Int. J. Climatol, 29(9): 1294-1311.
Forbes, R. and Ahlgrimm, M. (2015). Representing sub-grid heterogeneity of cloud and precipitation across scales, European Centre for Medium-Range Weather Forecasts, pp. 1-31.
Garcia, P.; Benarroch, A. and Riera, J.M. (2008). Spatial distribution of cloud cover, International Journal of Satellite Communications and Networking, 26(2): 141-155.
Haji mirrahimi, S. and Bai, N. (2008). Cloud types classification by applying the AVHRR granules, Geomatic Conference, Tehran.
Hubanks P.A.; Platnick, S.T.; King, M.I. and Ridgway, B.I. (2015). MODIS Atmosphere L3 Gridded Product Algorithm Theoretical Basis Document (ATBD) & Users Guide, ATBD Reference number ATBDMOD-3, NASA, pp. 1-120;
Hyer, E.J.; Reid, J.S. and Zhang, J. (2011). An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals, Atmospheric Measurement Techniques, 4(3): 379-408.
Jiang, Jonathan H.; Hui Su; Chengxing Zhai; Vincent S. Perun; Anthony Del Genio; Larissa S. Nazarenko and Leo J. Donner (2012). Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA, A‐Train satellite observations, Journal of Geophysical Research: Atmospheres, 117(14): 1-24.
King, M.D.; Menzel, W.P.; Kaufman, Y.J.; Tanre, D.; Gao, B.C.; Platnick, S.; Ackerman, S.; Remer, A.L.A.; Pincus, R. and Hubanks, P.A. (2003). Cloud and aerosol properties, perceptible water, and profiles of temperature and humidity, IEEE Trans. Geosci. Remote Sens., 41: 442-458.
King N J, Bower K N, Crosier J, and Crawford I, (2013). Evaluating MODIS cloud retrievals with in situ observations from VOCALS-Rex, Atmospheric Chemistry and Physics, 13(1):191-209.
Kotarba, A.Z. (2009). A comparison of MODIS-derived cloud amount with visual surface observations, Atmospheric Research, 92(4): 522-530.
Li, Z.; Cribb, M.C.; Chang, F.L. and Trishchenko, A.P. (2004). Validation of MODIS-retrieved cloud fractions using whole sky imager measurements at the three ARM sites, In Proceedings from 14th ARM Science Team Meeting, Albuquerque, New Mexico.
Li, Z.,; Li, J.; Menzel, W.P.; Schmit, T.J. and Ackerman, S.A. (2007). Comparison between current and future environmental satellite imagers on cloud classification using MODIS, Remote Sensing of Environment, 108(3): 311-326.
Masoodian, S.A and Kaviani, M.R. (2008). Climatology of Iran, Isfahan University publishing. Isfahan, Iran. 
Merat, A. (2007). Concepts and Statistics Methods, Book Publishing companies of Iran.
Naud, C.M.; Booth, J.F.; Posselt, D.J. and Heever, S.C. (2013). Multiple satellite observations of cloud cover in extratropical cyclones, Journal of Geophysical Research: Atmospheres, 118.17: 9982-9996.
Omidvar, k. (2001). The synoptic analysis of rainy systems and the possibility of increasing rainfall them by cloud seeding in Kerman regions, Geographical researches, 40: 19-32.
Platnick, S.; King, M.D.; Ackerman, S.; Paul, A.; Menzel, W.; Baum, B.A. and Frey, R.A. (2003). The MODIS cloud products: Algorithms and examples from Terra, IEEE Trans, Geosci, Remote Sens, 41: 459-473.
Rasooli, A.; Jahanbakhsh, S. and Ghasemi, A. (2013). Evaluation of spatial and temporal variations of the cloud cover amount in Iran, Geography Research Quarterly, 3(28): 85-102.
Rossow, W.B. and Schiffer R.A. (1991). ISCCP Cloud Data Products, Bull. Amer. Meteor. Soc., 72: 2-20.
Rossow, W. B., & Schiffer, R. A. (1999). Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society80(11), 2261-2287.
Schiffer, R.A. and Rossow, W.B. (1983). The International Satellite Cloud Climatology Project (ISCCP): The first project of the World Climate Research Programed, Bull. Amer. Meteorol. Soc., 64: 779-784.
Wylie, D.; Darren, L.; Jackson, W. ;  Menzel, P. and Bates; J. (2005). Trends in global cloud cover in two decades of HIRS observations, Journal of climate, 18(15): 3021-3031.